Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

Computational Analysis of Complex Beat-to-Beat Dynamics in Heart Cells

Contrary to the popular belief that the heart maintains a regular rhythm, healthy heartbeats fluctuate in a chaotic way. We now know that the fluctuations do not display uncorrelated randomness, but they contain long-range correlations and can be characterized by a fractal. This behavior supports the adaptability of the heart and may thus protect it from external stress. The fractal complexity is also found in the smallest parts of the heart: the cells. In the dawn of advanced pluripotent stem cell technology, producing independently beating cardiomyocytes in a laboratory, the beat-rate fluctuations of heart cells can be directly studied. In this thesis, we investigate the complex fluctuations in the field potentials generated by clusters of human cardiomyocytes. We show that the heart cells exhibit similar correlation properties in the beat-to-beat intervals and field potential durations comparable to RR and QT intervals, i.e., time between consecutive R waves and time from Q wave to the end of T wave, respectively, in an electrocardiogram of a heart. The cells are studied under conditions resembling real-life situations such as cardiac disorders, application of cardioactive drugs, and injuries. The results show significant alteration of the scaling properties in the beat rates, reflecting the changes in the intrinsic mechanism at the cellular level. By employing a set of nonlinear time series analysis tools, we explore their powerful applicability as well as their limitations. Our main method of choice throughout the work is detrended fluctuation analysis, which is designed to detect the degree of correlation in nonstationary time series. We demonstrate that detrended fluctuation analysis and its extensions are extremely useful in dealing with the field potential data of the heart cells despite the presence of abnormalities and irregular trends. The study of heartbeat dynamics at the cellular level using computational methods has important advantages. In particular, the methods provide non-invasive and versatile ways to improve our understanding of the intrinsic firing patterns of the heart cells, which play a crucial role in the future applications of in vitro human cardiomyocytes

Arrhythmia mechanisms in human induced pluripotent stem cell-derived cardiomyocytes

Despite major efforts by clinicians and researchers, cardiac arrhythmia remains a leading cause of morbidity and mortality in the world. Experimental work has relied on combining high-throughput strategies with standard molecular and electrophysiological studies, which are, to a great extent, based on the use of animal models. As this poses major challenges for translation, the progress in the development of novel antiarrhythmic agents and clinical care has been mostly disappointing. Recently, the advent of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has opened new avenues for both basic cardiac research and drug discovery: now there is an unlimited source of CMs of human origin, both from healthy individuals and patients with cardiac diseases. Understanding arrhythmic mechanisms is one the main use-cases of hiPSC-CMs, in addition to pharmacological cardiotoxicity and efficacy testing, in vitro disease modeling, developing patient-specific models and personalized drugs, and regenerative medicine. Here, we review the advances that the hiPSC-based modeling systems have brought so far regarding the understanding of both arrhythmogenic triggers and substrates, while also briefly speculating about the possibilities in the future.publishedVersionPeer reviewe

The application of qPCR for characterization of LQTS-specific cardiomyocytes derived from induced pluripotent stem cells

Background and aims: Long QT syndrome (LQTS) is an inherent cardiac disorder causing severe arrhythmias due to mutations in cardiac ion channel genes. Four founder mutations causing LQTS have been identified from Finland and they disrupt functions of cardiac potassium ion channels. The aim of this study was to characterize expressions of ion channel genes or their allelic expression variation in three different experiments by using qPCR. In cell line characterization experiment was evaluated applicability of cell lysis -based genotyping method to characterize cardiac cell lines. The aim of the second experiment was to evaluate impacts of allelic expression differences on disease phenotype variation and development. The last experiment was used for examining capacity of single-cell qPCR method to detect specific gene expression within single cardiomyocytes. Methods: qPCR was used to detect mRNA levels of ion channel genes or structural genes within LQTS-specific cardiomyocytes. The TaqMan® Sample-to-SNP™ Kit was tested in order to characterize KCNQ1 mutated heterozygous cardiac cell lines. Allelic imbalance determination was performed using plasmid-derived standard curve method for each Finnish founder mutations. The Single cell-to-CT™ Kit was used to detect expression of TNNT2 at the single cell level and cell population level. Results: The used cell lysis -based genotyping method succeeded to characterize cardiac cell lines and any disruption was not detected during analyses. Allelic imbalance determination study revealed that KCNQ1-FinA and KCNQ1-FinB specific cardiomyocytes expressed alleles of KCNQ1 with allelic ratios 3:1 indicating that expression of wild type allele was three-fold as compared to expression of mutant allele within these LQT1-specific cells. The Single cell-to-CT™ Kit did not manage to detect gene expression of TNNT2 within single cell samples and its expression at population level was also reduced. Conclusions: The detected allelic ratio 3:1 (WT:MUT) within KCNQ1-FinA and KCNQ1-FinB mutated cardiomyocytes could result in milder phenotypic effects due to that higher expression of wild type alleles may suppress effects of mutations. Milder phenotypic effects have been thought to be reason for unique enrichment of founder mutations among Finnish population. Therefore, allelic imbalance occurring in disease-causing genes can be a significant disease phenotype modifier. To validate this hypothesis, larger study population of mutation carriers with high phenotypic variation is needed. If correlation between allelic imbalance and phenotypic variation is detected, the allelic expression variation might be confirmed to be one explaining genetic factor to affect phenotypic variation. TIIVISTELMÄ: qPCR-menetelmän hyödyntäminen indusoiduista pluripotenteista kantasoluista erilaistettujen LQTS-spesifisten sydänlihassolujen karakterisoinnissa Tutkimuksen tausta ja tavoitteet: Pitkä QT oireyhtymä on vakavia rytmihäiriöitä aiheuttava perinnöllinen sairaus, joka johtuu mutaatioista sydänlihassolun ionikanavageeneissä. Suomessa vallitsee neljä pitkä QT oireyhtymää aiheuttavaa perustajamutaatiota, jotka heikentävät sydänlihassolujen kalium-ionikanavien toimintaa. Tämän tutkimuksen tavoitteena oli karakterisoida LQTS-spesifisten sydänlihassolujen ionikanavageenien ilmentymistä tai niiden alleeli-ilmentymiseroja hyödyntämällä qPCR-menetelmää kolmessa erillisessä työssä. Ensimmäisessä työssä arvioitiin solulyysaukseen perustuvan genotyyppausmenetelmän soveltuvuutta sydänsolulinjojen karakterisointiin. Toisen työn tavoitteena oli arvioida alleeli-ilmentymiserojen vaikutuksia tautifenotyypin vaihteluun ja kehittymiseen. Kolmannen työn tarkoituksena oli tutkia yksisolu-qPCR-menetelmän tehokkuutta havainnoida yksittäisten sydänlihassolujen geeni-ilmentymistä. Menetelmät: qPCR-menetelmää käytettiin jokaisessa työssä mittaamaan ionikanavageenien tai rakennegeenien mRNA-tasoja LQTS-spesifisistä sydänlihassoluista. Ensimmäisessä työssä käytettiin TaqMan® Sample-to-SNP™ Kit -menetelmää karakterisoimaan heterotsygoottisia sydänsolulinjoja, joissa oli KCNQ1-geenin mutaatio. Alleeli-ilmentymisvaihteluun perustuva tutkimus suoritettiin plasmideista tuotetun standardikuvaajan avulla jokaiselle Suomessa vallitsevalle perustajamutaatiolle. Viimeisessä työssä käytettiin Single cell-to-CT™ Kit -menetelmää, jonka avulla mitattiin TNNT2-geenin ilmentymistä sekä yksittäissolu- että solupopulaatiotasolla. Tulokset: Ensimmäisen työn tuloksista pääteltiin, että solulyysaukseen perustuva genotyyppausmenetelmä soveltuu sydänsolulinjojen karakterisointiin, eikä analyysiä häiritseviä tekijöitä havaittu. Alleeli-ilmentymiseen perustuva tutkimus paljasti, että KCNQ1-FinA ja KCNQ1-FinB spesifisissä sydänlihassoluissa KCNQ1-geenin alleelit ilmentyivät 3:1-suhteessa, joka tarkoitti sitä, että villityyppialleelien ilmentyminen oli kolminkertainen verrattuna mutanttialleelien ilmentymiseen näissä LQT1-spesifisissä soluissa. Single cell-to-CT™ Kit -menetelmällä ei onnistuttu havainnoimaan TNNT2-geenin ilmentymistä yksittäissolutasolla ja solupopulaatiotasolla sen ilmentyminen oli myös heikkoa. Johtopäätökset: Havaittu alleeli-ilmentymissuhde 3:1 (WT:MUT) KCNQ1-FinA ja KCNQ1-FinB mutatoituneissa sydänlihassoluissa voisi johtaa mutaatioiden heikentyneisiin fenotyyppivaikutuksiin, koska villityyppialleelin voimakkaampi ilmentyminen saattaa heikentää mutaatioiden vaikutuksia. Aiemmin on arveltu, että perustajamutaatioiden heikompi fenotyyppivaikutus on johtanut niiden yleistymiseen Suomessa. Tällöin taudinaiheuttajageenien alleelien ilmentymiserot saattavat vaikuttaa merkittävästi tautifenotyyppiin. Tämän varmistamiseksi on kuitenkin tutkittava alleeli-ilmentymiserot suuremmasta populaatiosta mutaationkantajia, joilla on havaittu suuri fenotyyppivaihtelu. Jos alleeli-imbalanssin ja fenotyypin välillä havaitaan korrelaatiota, voitaisiin alleeli-imbalanssi varmistaa yhdeksi fenotyyppivaihtelua selittäväksi geneettiseksi tekijäksi

Data analytics for cardiac diseases

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Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes Afford New Opportunities in Inherited Cardiovascular Disease Modeling

Fundamental studies of molecular and cellular mechanisms of cardiovascular disease pathogenesis are required to create more effective and safer methods of their therapy. The studies can be carried out only when model systems that fully recapitulate pathological phenotype seen in patients are used. Application of laboratory animals for cardiovascular disease modeling is limited because of physiological differences with humans. Since discovery of induced pluripotency generating induced pluripotent stem cells has become a breakthrough technology in human disease modeling. In this review, we discuss a progress that has been made in modeling inherited arrhythmias and cardiomyopathies, studying molecular mechanisms of the diseases, and searching for and testing drug compounds using patient-specific induced pluripotent stem cell-derived cardiomyocytes

The Effects of Pharmacological Compounds on Beat Rate Variations in Human Long QT-Syndrome Cardiomyocytes

Healthy human heart rate fluctuates overtime showing long-range fractal correlations. In contrast, various cardiac diseases and normal aging show the breakdown of fractal complexity. Recently, it was shown that human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) intrinsically exhibit fractal behavior as in humans. Here, we investigated the fractal complexity of hiPSC-derived long QT-cardiomyocytes (LQT-CMs). We recorded extracellular field potentials from hiPSC-CMs at baseline and under the effect of various compounds including β-blocker bisoprolol, ML277, a specific and potent IKs current activator, as well as JNJ303, a specific IKs blocker. From the peak-to-peak-intervals, we determined the long-range fractal correlations by using detrended fluctuation analysis. Electrophysiologically, the baseline corrected field potential durations (cFPDs) were more prolonged in LQT-CMs than in wildtype (WT)-CMs. Bisoprolol did not have significant effects to the cFPD in any CMs. ML277 shortened cFPD in a dose-dependent fashion by 11 % and 5-11 % in WT- and LQT-CMs, respectively. JNJ303 prolonged cFPD in a dose-dependent fashion by 22 % and 7-13 % in WT- and LQT-CMs, respectively. At baseline, all CMs showed fractal correlations as determined by short-term scaling exponent α. However, in all CMs, the α was increased when pharmacological compounds were applied indicating of breakdown of fractal complexity. These findings suggest that the intrinsic mechanisms contributing to the fractal complexity are not altered in LQT-CMs. The modulation of IKs channel and β1-adrenoreceptors by pharmacological compounds may affect the fractal complexity of the hiPSC-CMs